"When you feel how depressingly slowly you climb, it's well to remember that Things Take Time" -Piet Hein
"When you get the chance to sit it out or dance, I hope you dance"
-Mark D. Sanders & Tia Sillers
As of January, I have moved to University of Alberta as a Postdoctoral Fellow. I am part of the Intelligent Robot Learning Lab (IRL lab) and working with Professor Matthew Taylor . I completed my PhD in Computer Science from The University of Texas at Dallas under the supervision of Prof. Sriraam Natarajan . I was part of the StaRLing lab @ UTD .
My research interest spans across building sample-efficient models like Active Learning & Cost-sensitive learning to various decision making tasks like Reinforcement Learning, Inverse RL, Imitation learning etc in structured domains. I am also interested in leveraging human guidance to guide the sample-efficient and decision making models. Currently, I am interested in using human advice of different modalities and teacher-student framework to guide Reinforcement learning agents towards optimal behaviour more efficiently.
Attending the CIFAR Deep Learning and Reinforcement Learning Summer school 2022!!
Delighted to be co-organizing the Deep RL Workshop at NeurIPS 2021. Please consider submitting your best work!!
My dissertation can be found here.
Our paper on Cost Aware Feature Elicitation received the honorable mention for best paper in CODS-COMAD 2021. Congrats to my awesome co-authors.
Paper on test-time feature elicitation accepted to CODS-COMAD 2021
My story got featured here
I have successfully defended my dissertation on Aug 31. Thanks to my wonderful committee members
Paper on Cost aware learning accepted to KIML 2020 workshop co-located with KDD
Paper on Fitted -Q for Relational RL accepted as poster paper in KR 2020.
Passed the PhD Candidacy exam
Presented my poster on "Active Feature Elicitation" in WiML 2019, Vancouver, Canada
Co-organizer of the Deep RL Workshop at NeurIPS 2021
Volunteer at WiML 2019, Vancouver, Canada
Reviewer at :
Conference: SDM 2020; CODS-COMAD 2020; AISTATS 2021, AAAI 2021; 2022
Journals: Frontiers in Big Data, Data Mining & Knowledge Discovery (DMKD)